Adaptive Defense Against Packet-Mutation Adversarial Attacks in Network Intrusion

This paper presents a novel approach to defending against packet-mutation based adversarial attacks in Network Intrusion Detection Systems (NIDS). We introduce two key innovations: a genetic algorithm-based packet mutation technique for generating adversarial examples, and an adaptive defense mechanism that combines ensemble learning with meta-learning capabilities. Our defense system employs multiple base models (Random Forests, SVMs, LSTM networks, and CNNs) alongside a meta-learner that continuously adapts to evolving threats. Experimental results using the UNSW-NB15 dataset demonstrate that our adaptive defense system achieves a 93.7% detection rate, significantly outperforming traditional NIDS approaches which achieve only 52.3% detection rate. The system maintains high performance even under heavy network loads (up to 250,000 packets per second) with sub-second adaptation times. Notable improvements include a 43.4% increase in zero-day attack detection and a 39.4% improvement in detecting packet mutation attacks compared to traditional systems. The integration of meta-learning with ensemble methods proves particularly effective in addressing evolving attack patterns while maintaining low computational overhead.